overview

Shuiwang Ji is currently an Associate Professor in the Department of Computer Science & Engineering, Texas A&M University, leading the Data Integration, Visualization, and Exploration (DIVE) Laboratory. Ji received the Ph.D. degree in Computer Science from Arizona State University in 2010, advised by Prof. Jieping Ye. His research interests include machine learning, data mining, and computational neuroscience. Ji received the National Science Foundation CAREER Award in 2014. He has authored over 80 research articles and has coauthored a book. Currently, Ji serves as an Action Editor for Data Mining and Knowledge Discovery, and an Associate Editor for ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Neural Networks and Learning Systems, and BMC Bioinformatics. Ji is a Program Chair for the 2017 Bioimage Informatics Conference and a senior member of IEEE.

education and training
selected publications
Academic Articles63
  • Liu, M., Wang, Z., & Ji, S (2021). Non-Local Graph Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. PP(99), 1-1.
    doi badge
  • Yuan, H., Yang, F., Du, M., Ji, S., & Hu, X (2021). Towards structured NLP interpretation via graph explainers. Applied AI Letters. 2(4),
    doi badge
  • Cai, L., Wang, Z., Kulathinal, R., Kumar, S., & Ji, S (2021). Deep Low-Shot Learning for Biological Image Classification and Visualization From Limited Training Samples. IEEE Transactions on Neural Networks and Learning Systems. PP(99), 1-11.
    doi badge
  • Gao, H., Wang, Z., Cai, L., & Ji, S (2021). ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions.. IEEE Trans Pattern Anal Mach Intell. 43(8), 2570-2581.
    doi badge pubmed badge
  • Wang, Y., Cai, L., Chen, W., Wang, D., Xu, S., Wang, L., ... Xian, M (2021). Development of Xanthene‐Based Fluorescent Dyes: Machine Learning‐Assisted Prediction vs. TD‐DFT Prediction and Experimental Validation. Chemistry–Methods. 1(8), 389-396.
    doi badge
Books1
  • Sun, L., Ji, S., & Ye, J. (2016). Multi-Label Dimensionality Reduction.
Chapters1
  • Ye, J., & Ji, S (2009). Discriminant Analysis for Dimensionality Reduction: An Overview of Recent Developments. Biometrics. 1-19. John Wiley & Sons, Inc..
    doi badge
Conference Papers60
  • Dong, Y., Ding, K., Jalaian, B., Ji, S., & Li, J (2021). AdaGNN. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. 392-401.
    doi badge
  • Mao, H., Liu, X. i., Duffield, N., Yuan, H., Ji, S., & Mohanty, B. (2020). Context-Aware Deep Representation Learning for Geo-Spatiotemporal Analysis. 2020 IEEE International Conference on Data Mining (ICDM), 2020 IEEE International Conference on Data Mining (ICDM). 00, 392-401.
  • Wang, Z., Sisman, B., Wei, H., Dong, X. L., & Ji, S (2020). CorDEL: A Contrastive Deep Learning Approach for Entity Linkage. 2020 IEEE International Conference on Data Mining (ICDM), 2020 IEEE International Conference on Data Mining (ICDM). 00, 1322-1327.
    doi badge
  • Liu, Y. i., Yuan, H., Cai, L., & Ji, S (2020). Deep Learning of High-Order Interactions for Protein Interface Prediction. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 679-687.
    doi badge
  • Gao, H., Wang, Z., & Ji, S (2020). Kronecker Attention Networks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 229-237.
    doi badge
Repository Documents / Preprints5
  • Dong, Y., Ding, K., Jalaian, B., Ji, S., & Li, J (2021). AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter.
  • Wang, Z., Sisman, B., Wei, H., Dong, X. L., & Ji, S (2020). CorDEL: A Contrastive Deep Learning Approach for Entity Linkage.
  • Mohseni, S., Yang, F., Pentyala, S., Du, M., Liu, Y. i., Lupfer, N., ... Ragan, E (2020). Machine Learning Explanations to Prevent Overtrust in Fake News Detection.
  • Yang, F., Pentyala, S. K., Mohseni, S., Du, M., Yuan, H., Linder, R., ... Hu, X (2019). XFake: Explainable Fake News Detector with Visualizations.
chaired theses and dissertations
Email
sji@tamu.edu
First Name
Shuiwang
Last Name
Ji